Transcript CISBIC March 09 - Workspace

CISBIC:
A Centre for Integrative Systems Biology at Imperial College
TUBERCULOSIS MODELLING
Muna El-Khairi
CISBIC, Flowers Building, Imperial College London.
www.imperial.ac.uk/cisbic
Introduction
• Tuberculosis is a major cause of illness and
death worldwide
• Approximately one third of the world’s
population is infected with Mtb
• The majority of infected individuals experience
an asymptomatic state known as latency
• 5-10% of infected people develop active TB
within 1-5 years
Mycobacterium Tuberculosis
• Tubercle bacilli are able to switch between
replicating and non-replicating states in
response to host immunity
• This enables Mtb to persist in the host leading
to latent infection
Data
• Cattle immune response data available from
Veterinary Laboratories Agency
• Measure immune responses in peripheral blood
after experimental infection with M. bovis
• Temporal data from cattle infected with M. bovis
+/- treatment with anti-TB drug isoniazid
• Temporal data from cattle infected with M. bovis
and subject to drug treatment and re-challenge
with a different strain
• Pathology data
Data
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Drug treatment
Drug treatment
followed by rechallenge
Objectives
• To develop a model to predict the outcome of
drug treatment
• To identify factors which lead to differences in
the immune response of the cattle
• To make predictions about the response of the
cattle to re-challenge
Modelling
• Started working on ODE model describing T
cell population dynamics during infection with
M. bovis
• Model included populations of naïve T cells,
activated T cells, memory T cells and
mycobacteria
Modelling
• Currently working on ODE model describing
bacterial population dynamics
• Model includes populations of replicating and
dormant mycobacteria
• VLA data will be used to create an input
function for immune activity
Example
PPDB
7
IFN-gamma (OD 450nm)
6
3199
5
3200
3201
4
3202
714
3
718
724
2
734
1
0
-4
-2
0
2
4
6
Weeks PI
8
10
12
14
16
Example
3000 Series
7
IFN-gamma (OD 450nm)
6
5
4
PPDB
3
2
1
0
-2
0
2
4
6
8
10
12
14
Weeks PI
700 Series
7
IFN-gamma (OD 450nm)
6
5
4
PPDB
3
2
1
0
-4
-2
0
2
4
6
Weeks PI
8
10
12
14
16
Future Work
• Use Gaussian process regression to fit a
probabilistic model to the data
• Extend the model to incorporate drug
treatment
• Extend the model to incorporate drug
treatment followed by re-challenge
Acknowledgements
Funding by EPSRC
Imperial College London:
Jaroslav Stark
Douglas Young
Brian Robertson
Simon Moon
Piers Ingram
Michael Stumpf
Paul Kirk
Samantha Sampson
Veterinary Laboratories Agency:
Martin Vordermeier
Glyn Hewinson